NON-SMALL CELL LUNG CANCER ANALYSIS
Emile Cohen
June 2020
Goal: In this notebook, we want to understand what makes Colorectal Cancer a textbook case for the patterns we saw, and what are the major subcohorts that drive the signal.
In [15]:
%run -i '../../../../../utils/setup_environment.ipy'
import warnings
warnings.filterwarnings('ignore')
from scipy.stats import fisher_exact, ranksums, chi2, norm
from statsmodels.sandbox.stats.multicomp import multipletests
import matplotlib.gridspec as gridspec
import pickle
data_path = '../../../../../data/'
data_wgd = data_path + 'impact-facets-tp53/processed/wgd/'
data_no_wgd = data_path + 'impact-facets-tp53/processed/no_wgd/'
Interesting functions¶
In [16]:
from functools import reduce
def get_hotspots(df: pd.DataFrame, Sample_Type: str, group: list = None, group_type:str = None):
data = df[df['Sample_Type'] == Sample_Type]
if group and group_type:
data = data[data[group_type].isin(group)]
data_1 = get_groupby(data,'tp53_spot_1', 'count'); data_2 = get_groupby(data,'tp53_spot_2', 'count'); data_3 = get_groupby(data,'tp53_spot_3', 'count') ; data_4 = get_groupby(data,'tp53_spot_4', 'count') ; data_5 = get_groupby(data,'tp53_spot_5', 'count')
series_data = [data_1,data_2,data_3,data_4,data_5]
df_merged = reduce(lambda left,right: pd.merge(left,right,left_index=True, right_index=True,
how='outer'), series_data).fillna(0)
df_merged.columns = ['count_1', 'count_2', 'count_3', 'count_4', 'count_5']
df_merged['total'] = df_merged.sum(axis=1)
df_merged = df_merged.sort_values(by='total', ascending=False)
df_merged = df_merged.drop('nan')
return df_merged
def get_hotspot_frac(df: pd.DataFrame, group_type:str = None, group: list = None, nb = 10):
if group_type and group:
df = df[df[group_type].isin(group)]
result = [['spot', '#', 'frac']]
for spot in get_groupby(df, 'tp53_spot_1', 'count').sort_values(by='count', ascending=False).head(nb).index.tolist():
result.append([spot,df[df['tp53_spot_1'] == spot].frac_genome_altered.shape[0], df[df['tp53_spot_1'] == spot].frac_genome_altered.median()])
return pd.DataFrame(result)
def boxplot_sampletype(df: pd.DataFrame, group:str, palette, order, metrics: str, figsize= (10,3), title: str = '', title_font: int=12, xlim=[0,1]):
fig=plt.figure(figsize=figsize)
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
sns.boxplot(y=metrics, x=group,data=df,ax=ax, dodge=False,order=order, palette=palette).set_title(title, weight='bold', fontsize=title_font)
groupby_ = get_groupby(df,group, 'count')
groupby_ = groupby_.T
for mut in mutation_list:
if mut not in groupby_.columns:
groupby_[mut] = 0
groupby_ = groupby_.T
labels = []
for element in order:
labels.append(element + '\n('+ str(groupby_.loc[element].values[0])+')')
ax.set_xticklabels(labels)
style(ax)
ax.set_ylim(xlim)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
return fig, ax
# Let's give a look at medians and statistics
def get_statistics(df: pd.DataFrame, group:str, metrics: str, group_list: list):
group_1 = df[df[group] == group_list[0]][metrics]
group_2 = df[df[group] == group_list[1]][metrics]
median_1 = group_1.median()
median_2 = group_2.median()
statistic, p_value = ranksums(group_1.dropna().values,group_2.dropna().values)
results = [['', 'size', metrics],
[group_list[0], group_1.shape[0], median_1],
[group_list[1], group_2.shape[0], median_2],
['', 'Statistics', 'p-value'],
['', statistic, p_value]]
return pd.DataFrame(results)
def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10, tp53=False):
samples = master.Tumor_Id.tolist()
if tp53:
maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True]
else:
maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
h.columns = ['count']
h = h.sort_values(by='count', ascending=False).head(head)
return(h)
def create_co_drivers_table(master: pd.DataFrame, group_type:str, group_1: str, group_2: str):
master_group_1 = master[master[group_type] == group_1]
co_drivers_group_1 = get_major_codrivers(master=master_group_1,
maf=maf_cohort_nowgd,
head=100)
co_drivers_group_1['proportion_1'] = co_drivers_group_1.apply(lambda x: 100* round(x['count'] / co_drivers_group_1.sum().values[0], 4), axis=1)
master_group_2 = master[master[group_type] == group_2]
co_drivers_group_2 = get_major_codrivers(master=master_group_2,
maf=maf_cohort_nowgd,
head=100)
co_drivers_group_2['proportion_2'] = co_drivers_group_2.apply(lambda x: 100* round(x['count'] / co_drivers_group_2.sum().values[0], 4), axis=1)
co_drivers_groups = pd.merge(co_drivers_group_1, co_drivers_group_2, on='Hugo_Symbol')
co_drivers_groups['proportion_1'] = - co_drivers_groups['proportion_1']
return co_drivers_groups
Master Definition and Filtering¶
In [17]:
cancer = 'Non-Small Cell Lung Cancer'
In [18]:
master_no_wgd = non_wgd_load_and_cut(data_path + 'impact-facets-tp53/processed/no_wgd/master_no_wgd.pkl')
master_wgd = pd.read_pickle(data_path + 'impact-facets-tp53/processed/wgd/master_wgd.pkl')
master_no_wgd_cancer = master_no_wgd[master_no_wgd['Cancer_Type'] == cancer]
master_wgd_cancer = master_wgd[master_wgd['Cancer_Type'] == cancer]
maf_cohort_nowgd = pd.read_csv(data_path + 'impact-facets-tp53/processed/no_wgd/maf_cohort_nowgd.txt', sep='\t').drop('Unnamed: 0', axis=1)
maf_cohort_wgd = pd.read_csv(data_path + 'impact-facets-tp53/processed/wgd/maf_cohort_wgd.txt', sep='\t').drop('Unnamed: 0', axis=1)
In [19]:
master_wgd[master_wgd['tp53_count'] >=1]
Out[19]:
In [13]:
get_groupby(master_wgd[master_wgd['tp53_count'] >=1],'tp53_cn_state', 'count')[:4].sum()
Out[13]:
What makes Colorectal Cancer a TextBook Case?¶
WGD Proportion¶
Non-Small Cell Lung Cancer is one of the biggest cancer types in our cohort. It is the fourth cancer in term of WGD proportion with an average proportion of WGD - around 42%
Cancer Panel¶
- NSCLC is the third most represented cancer in MSK-Impact Cohort.
- Slightly enriched for Primary Samples
- In comparison with other cancer types, Non-Small Cell Lung Cancer is not particularly enriched nor depleted for a tp53 subgroup.
Genome Instability¶
Non-Small Cell Lung Cancer Cancer shows a significant difference in Genome Instability between TP53 Mono-Allelic and Bi-Allelic subgroups - and has a lot of samples in both groups.
In the TP53 subgroup Pan Cancer plot that follows, we can see 3 important signals:
- The GI difference between 0_HETLOSS and 1_WILD_TYPE, two likely mono-allelic subgroups
- 1_WILD_TYPE GI is very low compared to other subgroups
- Bi Allelic Subgroups - >=1_LOSS and >=1_cnLOH - have higher GI than other subgroups and the difference is significant
WGD Part¶
Subgroup Proportion¶
2 main key points:
- No enrichment in Pre-WGD TP53 Bi-Allelic for NSCLC
- Enrichment in tp53 mutation and TP53 LOH samples
Very High Genome Instability¶
Non WGD Part - Cancer Investigation¶
In this section, our goal is to find subcohorts that lead the signals observed. Here are the different subcohort we will create:
- Hotspot Analysis: splitting on 273 / 248 / 175 / Missense / Truncated / In Frame
- CCF Analysis
- SNV/INDEL Analysis
Hotspot Analysis¶
In this section, we cut our cohort to only keep samples with exactly one TP53 mutation, for simplicity.
In [8]:
master_hotspot = master_no_wgd_cancer[master_no_wgd_cancer['tp53_count'] == 1]
In [9]:
get_hotspot_frac(df=master_hotspot,
group_type=None,
group=None)
Out[9]:
Entire Cohort¶
In [13]:
h = get_groupby(master_hotspot,'tp53_vc_group_1', 'count').sort_values(by='count', ascending=False)
display(h)
h = h.T
h = h[mutation_list]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.2, 0.5), fontsize=11)
ax.set_title('Mutation Type - {} - No WGD'.format(cancer), weight='bold', fontsize=18)
plt.show()
- NSCLC has a very small proportion of hotspots.
In [10]:
fig, ax = boxplot_sampletype(df=master_hotspot,
group='tp53_vc_group_1',
palette=mutation_palette,
order=mutation_list,
metrics='frac_genome_altered',
figsize=(6,10),
title='Fraction of Genome Altered - {}'.format(cancer),
xlim=[0,1])
plt.show()
- Once more, it seems that 273 hotspot leads to lower GI
TP53 Residual Subgroups¶
In [34]:
print('Number of Bi Allelic samples (with 1 mut): ' + str(master_hotspot[master_hotspot['tp53_res_group'] == 'no_tp53_res'].shape[0]))
print('')
print('Number of TP53 Residual samples (with 1 mut): ' + str(master_hotspot[master_hotspot['tp53_res_group'] == 'tp53_res'].shape[0]))
In [41]:
total_df = []
for group in ['tp53_res', 'no_tp53_res']:
h = get_groupby(master_hotspot[master_hotspot['tp53_res_group'] == group], 'tp53_vc_group_1', group).sort_values(by=group, ascending=False)
total_df.append(h)
h=h.T
for mut in mutation_list:
if mut not in h.columns:
h[mut] = 0
h = h[mutation_list]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
if group == 'tp53_res':
ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.05, 0.5), fontsize=11)
else: ax.get_legend().remove()
ax.set_title('Mutation Type - {} - No WGD'.format(group), weight='bold', fontsize=18)
plt.show()
display_side_by_side(total_df[0],total_df[1])
In [44]:
for group in ['tp53_res', 'no_tp53_res']:
master_wt = master_hotspot[master_hotspot['tp53_res_group'] == group]
fig, ax = boxplot_sampletype(df=master_wt,
group='tp53_vc_group_1',
palette=mutation_palette,
order=mutation_list,
metrics='frac_genome_altered',
figsize=(6,10),
title='Fraction of Genome Altered - No WGD - {} subgroup'.format(group),
xlim=[0,1])
plt.show()
SNV / INDEL Analysis¶
In this section we compare SNV and INDEL mutations. As in the previous section, we cut the cohort to keep only samples with exactly 1 tp53 mutation.
# of Drivers / SNV Drivers / INDEL Drivers¶
In [14]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
group='tp53_group',
palette=palette,
order=group_list,
metrics='driver_mutation_count',
figsize=(8,12),
title='Driver Mutation Count - TP53 Subroups - No WGD',
xlim=[-0.1,10])
plt.show()
display_side_by_side(get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='driver_mutation_count',
group_list=['1_WILD_TYPE', '0_HETLOSS']),
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='driver_mutation_count',
group_list=['1_WILD_TYPE', '>=1_LOSS']),
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='driver_mutation_count',
group_list=['>1muts', '>=1_LOSS']))
In [15]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
group='tp53_group',
palette=palette,
order=group_list,
metrics='snv_driver_mutation_count',
figsize=(8,12),
title='SNV Driver Mutation Count - TP53 Subroups - No WGD',
xlim=[-0.1,15])
plt.show()
display_side_by_side(get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='snv_driver_mutation_count',
group_list=['1_WILD_TYPE', '0_HETLOSS']),
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='snv_driver_mutation_count',
group_list=['1_WILD_TYPE', '>=1_LOSS']),
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='snv_driver_mutation_count',
group_list=['>1muts', '>=1_LOSS']))
In [16]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
group='tp53_group',
palette=palette,
order=group_list,
metrics='indel_driver_mutation_count',
figsize=(8,12),
title='INDEL Driver Mutation Count - TP53 Subroups - No WGD',
xlim=[-0.1,35])
plt.show()
display_side_by_side(get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='indel_driver_mutation_count',
group_list=['1_WILD_TYPE', '0_HETLOSS']),
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='indel_driver_mutation_count',
group_list=['1_WILD_TYPE', '>=1_LOSS']),
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='indel_driver_mutation_count',
group_list=['>1muts', '>=1_LOSS']))
Here, one major information:
- In NSCLC, there are very few numbers of Co-Drivers, INDEL Co-Drivers nor SNV Co-Drivers.
- Maybe TP53 mutations in NSCLC are very pervasvie and do not need more drivers to expand
Genome Instability¶
The idea here is to see if we have differences in Fraction of Genome Altered if we cut our Cancer cohort on the number of drivers per sample.
Do we have more instability with more INDEL Driver Mutations within the same subgroup?
1_WILD_TYPE Subgroup¶
In [19]:
master_no_wgd_cancer_wt = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '1_WILD_TYPE']
thr=1
def get_driver_groups(x):
if x.indel_driver_mutation_count > thr:
return 'High Co-Driver Count'
if x.indel_driver_mutation_count <= thr:
return 'Low Co-Driver Count'
master_no_wgd_cancer_wt['co_driver_group'] = master_no_wgd_cancer_wt.apply(get_driver_groups, axis=1)
In [20]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_wt,
group='co_driver_group',
palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
order=['High Co-Driver Count', 'Low Co-Driver Count'],
metrics='frac_genome_altered',
figsize=(4,10),
title='Fraction of Genome Altered - 1_WILD_TYPE subgroup - Co Driver Count (thr={}) - {}'.format(thr,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_no_wgd_cancer_wt,
group='co_driver_group',
metrics='frac_genome_altered',
group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[20]:
0_HETLOSS¶
In [21]:
master_no_wgd_cancer_het = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '0_HETLOSS']
thr=1
def get_driver_groups(x):
if x.indel_driver_mutation_count > thr:
return 'High Co-Driver Count'
if x.indel_driver_mutation_count <= thr:
return 'Low Co-Driver Count'
master_no_wgd_cancer_het['co_driver_group'] = master_no_wgd_cancer_het.apply(get_driver_groups, axis=1)
In [22]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_het,
group='co_driver_group',
palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
order=['High Co-Driver Count', 'Low Co-Driver Count'],
metrics='frac_genome_altered',
figsize=(4,10),
title='Fraction of Genome Altered - 0_HETLOSS subgroup - Co Driver Count (thr={}) - {}'.format(thr,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_no_wgd_cancer_het,
group='co_driver_group',
metrics='frac_genome_altered',
group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[22]:
In [ ]:
Co Driver Analysis¶
In [29]:
codrivers_cancer = get_major_codrivers(master=master_no_wgd_cancer,
maf=maf_cohort_nowgd,
head=15)
codrivers_cancer_tp53 = get_major_codrivers(master=master_no_wgd_cancer[master_no_wgd_cancer['tp53_count'] >= 1],
maf=maf_cohort_nowgd,
head=15)
In [30]:
co_drivers = pd.merge(codrivers_cancer, codrivers_cancer_tp53, on='Hugo_Symbol')
co_drivers.columns = ['cancer', 'cancer_tp53']
In [31]:
co_drivers['ratio'] = co_drivers.apply(lambda x: 100*round(x.cancer_tp53/x.cancer, 4) , axis=1)
co_drivers = co_drivers.sort_values(by='ratio', ascending=False)
In [32]:
co_drivers
Out[32]:
In [34]:
codrivers_cancer
Out[34]:
In [74]:
labels = []
codrivers_cancer = get_major_codrivers(master=master_no_wgd_cancer,
maf=maf_cohort_nowgd,
head=15,
tp53=True)
codrivers_cancer['proportion'] = codrivers_cancer.apply(lambda x: 100* round(x['count'] / codrivers_cancer.sum().values[0], 4), axis=1)
for element in codrivers_cancer.head(15).index.tolist():
labels.append(element + ' ('+ str(int(codrivers_cancer.loc[element]['count']))+')')
ax = sns.barplot(y=codrivers_cancer.head(15).index, x='proportion',data=codrivers_cancer.head(15)[['proportion']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Drivers Frequency in {}'.format(cancer))
Out[74]:
In [33]:
labels = []
for element in co_drivers.index.tolist():
labels.append(element + ' ('+ str(int(co_drivers.loc[element]['cancer']))+')')
ax = sns.barplot(y=co_drivers.index, x='ratio',data=co_drivers[['ratio']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Co-Drivers Enrichment in TP53 State')
Out[33]:
Co-Drivers per subgroup¶
In [87]:
co_drivers_res = create_co_drivers_table(master=master_no_wgd_cancer,
group_type='tp53_res_group',
group_1='tp53_res',
group_2='no_tp53_res')
co_drivers_res
Out[87]:
In [136]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
co_drivers_res[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = ['#2ECC71','#1E8449'])
ax.legend(['TP53 Residual', 'No TP53 Residual'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)
plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [20, 15, 10, 5, 0, 5, 10, 15]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)
plt.show()
In [36]:
co_drivers_cnloh_loss = create_co_drivers_table(master=master_no_wgd_cancer,
group_type='tp53_group',
group_1='>=1_cnLOH',
group_2='>=1_LOSS')
co_drivers_cnloh_loss
Out[36]:
In [37]:
labels = []
for element in co_drivers_cnloh_loss.head(10).index.tolist():
labels.append(element + ' ('+ str(int(co_drivers_cnloh_loss.loc[element]['count_x']))+')')
ax = sns.barplot(y=co_drivers_cnloh_loss.head(10).index, x='proportion_1',data=co_drivers_cnloh_loss.head(10)[['proportion_1']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Co-Drivers Frequency in {} - {}'.format(subgroup, cancer))
In [41]:
fig=plt.figure(figsize=(8,8))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
co_drivers_cnloh_loss[['proportion_1', 'proportion_2']].head(15)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[4],mc[0]])
ax.legend(['>=1_cnLOH', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)
plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [-10, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)
plt.show()
- The big picture is the same for both cancers
- Enrichment in PTEN and RASA1 / PBRM1 for >=1_cnLOH
In [47]:
co_drivers_losses = create_co_drivers_table(master=master_no_wgd_cancer,
group_type='tp53_group',
group_1='0_HETLOSS',
group_2='>=1_LOSS')
co_drivers_losses
Out[47]:
In [51]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
co_drivers_losses[['proportion_1', 'proportion_2']].head(15)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[5],mc[0]])
ax.legend(['0_HETLOSS', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)
plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [-20, -15, -10, -5, 0, 5, 10, 15]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)
plt.show()
- Enrichment in EGFR, KRAS, STK11 for 0_HETLOSS
In [48]:
co_drivers_mult_cnloh = create_co_drivers_table(master=master_no_wgd_cancer,
group_type='tp53_group',
group_1='>1muts',
group_2='>=1_cnLOH')
co_drivers_mult_cnloh
Out[48]:
In [52]:
get_major_codrivers(master=master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>1muts'],
maf=maf_cohort_nowgd,
head=100)
Out[52]:
In [53]:
fig=plt.figure(figsize=(10,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
co_drivers_mult_cnloh[['proportion_1', 'proportion_2']].head(20)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[3],mc[4]])
ax.legend(['>1muts', '>=1_cnLOH'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=15)
plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
print(a)
#a = [-20, -10, 0, 10, 20, 30, 40]
#ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)
plt.show()
* Hard to interpret as we have fewersamples in >1muts subgroup¶
In [57]:
co_drivers_wt_loss = create_co_drivers_table(master=master_no_wgd_cancer,
group_type='tp53_group',
group_1='1_WILD_TYPE',
group_2='0_HETLOSS')
co_drivers_wt_loss
Out[57]:
In [60]:
fig=plt.figure(figsize=(10,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
co_drivers_wt_loss[['proportion_1', 'proportion_2']].head(20)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[2],mc[5]])
ax.legend(['1_WILD_TYPE', '0_HETLOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=15)
plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [-20, -15, -10, -5, 0, 5, 10, 15, 20]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)
plt.show()
Comparison with WGD Cohort (WGD - TP53 - LOH)¶
In [61]:
def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10):
samples = master.Tumor_Id.tolist()
maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
h.columns = ['count']
h = h.sort_values(by='count', ascending=False).head(head)
return(h)
def create_co_drivers_table_wgd(master_1: pd.DataFrame, master_2: pd.DataFrame, group_type:str, group_1: str):
master_group_1 = master_1[master_1[group_type] == group_1]
co_drivers_group_1 = get_major_codrivers(master=master_group_1,
maf=maf_cohort_nowgd,
head=100)
co_drivers_group_1['proportion_1'] = co_drivers_group_1.apply(lambda x: 100* round(x['count'] / co_drivers_group_1.sum().values[0], 4), axis=1)
master_group_2 = master_2[master_2['tp53_count'] >=1][master_2['tp53_loh_status'] == True]
co_drivers_group_2 = get_major_codrivers(master=master_group_2,
maf=maf_cohort_wgd,
head=100)
co_drivers_group_2['proportion_2'] = co_drivers_group_2.apply(lambda x: 100* round(x['count'] / co_drivers_group_2.sum().values[0], 4), axis=1)
co_drivers_groups = pd.merge(co_drivers_group_2, co_drivers_group_1, on='Hugo_Symbol')
co_drivers_groups['proportion_2'] = - co_drivers_groups['proportion_2']
return co_drivers_groups
In [62]:
co_drivers_wgd_loss = create_co_drivers_table_wgd(master_1=master_no_wgd_cancer,
master_2=master_wgd_cancer,
group_type='tp53_group',
group_1='>=1_LOSS')
co_drivers_wgd_loss
Out[62]:
In [69]:
fig=plt.figure(figsize=(8,8))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
co_drivers_wgd_loss[['proportion_2', 'proportion_1']].head(15)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = ['#7F8C8D',mc[0]])
ax.legend(['WGD - TP53 - LOH', '>=1_LOSS'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)
plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [-20, -15, -10, -5, 0, 5, 10, 15]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)
plt.show()
Genome Instability Associated¶
In [101]:
def get_master_codrivers(master: pd.DataFrame, maf: pd.DataFrame, symbol: str):
samples = master.Tumor_Id.tolist()
samples_final = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['Hugo_Symbol'] == symbol].Tumor_Sample_Barcode.tolist()
master_filtered = master[master.Tumor_Id.isin(samples_final)]
return master_filtered
>=1_cnLOH¶
In [73]:
master_no_wgd_cancer_cnloh = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>=1_cnLOH']
master_APC = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
maf=maf_cohort_nowgd,
symbol='APC')
master_KRAS = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
maf=maf_cohort_nowgd,
symbol='KRAS')
master_no_wgd_cancer_cnloh['data'] = '>=1_cnLOH'
master_APC['data'] = 'APC'
master_KRAS['data'] = 'KRAS'
master_PIK3CA['data'] = 'PIK3CA'
masters = [master_no_wgd_cancer_cnloh, master_APC, master_KRAS, master_PIK3CA]
allMasters = pd.concat(masters)
In [74]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >=1_cnLOH')
ax.set_xlabel('')
Out[74]:
>=1_LOSS¶
In [78]:
master_no_wgd_cancer_loss = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>=1_LOSS']
master_APC = get_master_codrivers(master=master_no_wgd_cancer_loss,
maf=maf_cohort_nowgd,
symbol='APC')
master_KRAS = get_master_codrivers(master=master_no_wgd_cancer_loss,
maf=maf_cohort_nowgd,
symbol='KRAS')
master_PIK3CA = get_master_codrivers(master=master_no_wgd_cancer_loss,
maf=maf_cohort_nowgd,
symbol='PIK3CA')
master_no_wgd_cancer_loss['data'] = '>=1_loss'
master_APC['data'] = 'APC'
master_KRAS['data'] = 'KRAS'
master_PIK3CA['data'] = 'PIK3CA'
masters = [master_no_wgd_cancer_loss, master_APC, master_KRAS, master_PIK3CA]
allMasters = pd.concat(masters)
In [79]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >=1_LOSS')
ax.set_xlabel('')
Out[79]:
>1muts¶
In [77]:
master_no_wgd_cancer_muts = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>1muts']
master_APC = get_master_codrivers(master=master_no_wgd_cancer_muts,
maf=maf_cohort_nowgd,
symbol='APC')
master_KRAS = get_master_codrivers(master=master_no_wgd_cancer_muts,
maf=maf_cohort_nowgd,
symbol='KRAS')
master_KMT2D = get_master_codrivers(master=master_no_wgd_cancer_muts,
maf=maf_cohort_nowgd,
symbol='KMT2D')
master_RNF43 = get_master_codrivers(master=master_no_wgd_cancer_muts,
maf=maf_cohort_nowgd,
symbol='RNF43')
master_no_wgd_cancer_muts['data'] = '>1muts'
master_APC['data'] = 'APC'
master_KRAS['data'] = 'KRAS'
master_KMT2D['data'] = 'KMT2D'
master_RNF43['data'] = 'RNF43'
masters = [master_no_wgd_cancer_muts, master_APC, master_KRAS, master_KMT2D, master_RNF43]
allMasters = pd.concat(masters)
In [76]:
#### >1mutsfig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >1muts')
ax.set_xlabel('')
Out[76]:
1_WILD_TYPE¶
In [105]:
master_no_wgd_cancer_wt = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '1_WILD_TYPE']
master_KRAS = get_master_codrivers(master=master_no_wgd_cancer_wt,
maf=maf_cohort_nowgd,
symbol='KRAS')
master_EGFR = get_master_codrivers(master=master_no_wgd_cancer_wt,
maf=maf_cohort_nowgd,
symbol='EGFR')
master_CDKN2A = get_master_codrivers(master=master_no_wgd_cancer_wt,
maf=maf_cohort_nowgd,
symbol='CDKN2A')
master_no_wgd_cancer_wt['data'] = '1_WT'
master_KRAS['data'] = 'KRAS'
master_EGFR['data'] = 'EGFR'
master_CDKN2A['data'] = 'CDKN2A'
masters = [master_no_wgd_cancer_wt, master_EGFR, master_KRAS, master_CDKN2A]
allMasters = pd.concat(masters)
In [108]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - 1_WT')
ax.set_xlabel('')
Out[108]:
In [ ]:
CCF / VAF Analysis¶
Same here we take only samples with exactly 1 tp53 mutation (master_hotspot).
We have to define groups for CCF to see if there are differences between those groups. To have an idea of the CCF distribution we show here the distribution coming from the cancer_panel.
We see that our tp53_ccf distribution is very high for all subgroups except for 1_WILD_TYPEAdd and >1muts. >=1_LOSS is the biggest subgroup - by far - and has a very high CCF median.
It will be hard to cut the cohort based on the CCF. Let's try and see the size of the subcohorts:
In [20]:
master_ccf = master_no_wgd_cancer[(master_no_wgd_cancer['tp53_count'] == 1) | (master_no_wgd_cancer['tp53_group'] == '0_HETLOSS')]
In [22]:
thr_ccf_1 = 0.8 ; thr_ccf_2 = 0.9
def ccf_subgroup(x):
if x.tp53_ccf_1 <= thr_ccf_1: return 'low'
elif x.tp53_ccf_1 <= thr_ccf_2: return 'medium'
elif x.tp53_ccf_1 > thr_ccf_2: return 'high'
master_ccf['ccf_group'] = master_ccf.apply(ccf_subgroup, axis=1)
get_groupby(master_ccf, 'ccf_group', 'count')
Out[22]:
In [83]:
thr_vaf_1 = 0.3 ; thr_vaf_2 = 0.4
def vaf_subgroup(x):
if x.tp53_vaf_1 <= thr_vaf_1: return 'low'
elif x.tp53_vaf_1 <= thr_vaf_2: return 'medium'
elif x.tp53_vaf_1 > thr_vaf_2: return 'high'
master_ccf['vaf_group'] = master_ccf.apply(vaf_subgroup, axis=1)
get_groupby(master_ccf, 'vaf_group', 'count')
Out[83]:
VAF Analysis¶
No VAF Cut¶
In [61]:
fig, ax = boxplot_sampletype(df=master_ccf,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - {}'.format(cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_ccf,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['0_HETLOSS', '1_WILD_TYPE'])
Out[61]:
Low VAF¶
In [62]:
master_low = master_ccf[(master_ccf['vaf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]
fig, ax = boxplot_sampletype(df=master_low,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - VAF < {} - {}'.format(thr_vaf_1,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_low,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['1_WILD_TYPE', '>=1_LOSS'])
Out[62]:
Medium VAF¶
In [63]:
master_med = master_ccf[(master_ccf['vaf_group'] == 'medium') | (master_ccf['tp53_group'] == '0_HETLOSS')]
fig, ax = boxplot_sampletype(df=master_med,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - {} < VAF < {} - {}'.format(thr_vaf_1,thr_vaf_2,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_med,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['1_WILD_TYPE', '0_HETLOSS'])
High VAF¶
In [64]:
master_high = master_ccf[(master_ccf['vaf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]
fig, ax = boxplot_sampletype(df=master_high,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - VAF > {} - {}'.format(thr_vaf_2,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_high,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['1_WILD_TYPE', '0_HETLOSS'])
CCF Analysis¶
No CCF Cut¶
In [65]:
fig, ax = boxplot_sampletype(df=master_ccf,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - {}'.format(cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_ccf,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['0_HETLOSS', '1_WILD_TYPE'])
Out[65]:
Low CCF¶
In [66]:
master_low = master_ccf[(master_ccf['ccf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]
fig, ax = boxplot_sampletype(df=master_low,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - CCF < {} - {}'.format(thr_ccf_1,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_low,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[66]:
Medium CCF¶
In [67]:
master_med = master_ccf[(master_ccf['ccf_group'] == 'medium') | (master_ccf['tp53_group'] == '0_HETLOSS')]
fig, ax = boxplot_sampletype(df=master_med,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - {} < CCF < {} - {}'.format(thr_ccf_1,thr_ccf_2,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_med,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[67]:
High CCF¶
In [84]:
master_high = master_ccf[(master_ccf['ccf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]
fig, ax = boxplot_sampletype(df=master_high,
group='tp53_group',
palette=palette,
order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
metrics='frac_genome_altered',
figsize=(5,10),
title='Fraction of Genome Altered - CCF > {} - {}'.format(thr_ccf_2,cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_high,
group='tp53_group',
metrics='frac_genome_altered',
group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[84]:
let's check the hotspot distribution
In [86]:
get_hotspot_frac(df=master_high[master_high['tp53_group'] == '1_WILD_TYPE'],
group_type=None,
group=None)
Out[86]:
Splitting on the different level of CCF / VAF¶
In [ ]:
fig, ax = boxplot_sampletype(df=master_hotspot,
group='vaf_group',
palette={'low': tab10[0] , 'medium': tab10[1], 'high':tab10[2]},
order=['low', 'medium', 'high'],
metrics='frac_genome_altered',
figsize=(3,10),
title='Fraction of Genome Altered - VAF levels - {}'.format(cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_hotspot,
group='vaf_group',
metrics='frac_genome_altered',
group_list=['low', 'medium'])
In [ ]:
fig, ax = boxplot_sampletype(df=master_hotspot,
group='ccf_group',
palette={'low': tab10[0] , 'medium': tab10[1], 'high':tab10[2]},
order=['low', 'medium', 'high'],
metrics='frac_genome_altered',
figsize=(3,10),
title='Fraction of Genome Altered - CCF levels - {}'.format(cancer),
xlim=[0,1])
plt.show()
get_statistics(df=master_hotspot,
group='ccf_group',
metrics='frac_genome_altered',
group_list=['low', 'medium'])
Clinical Correlates¶
Age¶
In [15]:
#### TP53 Residual Groups#fig=plt.figure(figsize=(10,3))
ax = plt.subplot2grid(shape=(4,1), loc=(0,0), colspan=1)
sns.boxplot(x='Patient_Current_Age',data=master_no_wgd_cancer, ax=ax).set_title('Patient Age - {}'.format(cancer), weight='bold', fontsize=14)
style(ax)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
TP53 Residual Groups¶
In [16]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
group='tp53_res_group',
palette=palette_res,
order=res_group_list,
metrics='Patient_Current_Age',
figsize=(3,10),
title='Patient Current Age - {}'.format(cancer),
xlim=[20,100])
plt.show()
get_statistics(df=master_no_wgd_cancer,
group='tp53_res_group',
metrics='Patient_Current_Age',
group_list=['tp53_res', 'no_tp53_res'])#### TP53 Subgroups
Out[16]:
TP53 Subgroups¶
In [17]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
group='tp53_group',
palette=palette,
order=group_list,
metrics='Patient_Current_Age',
figsize=(7,10),
title='Patient Current Age - {}'.format(cancer),
xlim=[20,100])
plt.show()
get_statistics(df=master_no_wgd_cancer,
group='tp53_group',
metrics='Patient_Current_Age',
group_list=['1_WILD_TYPE', '>=1_cnLOH'])
Out[17]:
Sex¶
In [18]:
h = get_groupby(master_no_wgd_cancer,'Sex', 'count').sort_values(by='count', ascending=False)
display(h)
h = h.T
h = h[['Male', 'Female']]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax)
ax.legend(['Male', 'Female'],loc='center left', bbox_to_anchor=(1.1, 0.5), fontsize=11)
ax.set_title('Sex Distribution - {} - No WGD'.format(cancer), weight='bold', fontsize=18)
plt.show()
Survival Analysis¶
In [25]:
from lifelines import KaplanMeierFitter
from lifelines.statistics import logrank_test
In [99]:
data = master_no_wgd_cancer.dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
data = data[['tp53_group', 'tp53_res_group', 'Overall Survival Status 0/1', 'Overall_Survival_Months']]
ix1 = data['tp53_res_group'] == 'tp53_res'
ix2 = data['tp53_res_group'] == 'no_tp53_res'
T_exp, E_exp = data.loc[ix1, 'Overall_Survival_Months'], data.loc[ix1, 'Overall Survival Status 0/1']
T_con, E_con = data.loc[ix2, 'Overall_Survival_Months'], data.loc[ix2, 'Overall Survival Status 0/1']
results = logrank_test(T_exp, T_con, event_observed_A=E_exp, event_observed_B=E_con)
results.print_summary()
In [23]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(res_group_list, range(len(res_group_list))):
data = master_no_wgd_cancer[master_no_wgd_cancer['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
plt.show()
In [24]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(group_list, range(len(group_list))):
data = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = palette_list[i], ax=ax)
plt.show()
HIGH CCF¶
In [26]:
master_high = master_ccf[(master_ccf['ccf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]
In [27]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - High CCF (CCF>0.9)'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(res_group_list, range(len(res_group_list))):
data = master_high[master_high['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
except: pass
plt.show()
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - High CCF (CCF>0.9)'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(group_list, range(len(group_list))):
data = master_high[master_high['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = palette_list[i], ax=ax)
except: pass
plt.show()
LOW CCF¶
In [28]:
master_low = master_ccf[(master_ccf['ccf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]
In [29]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - Low CCF (CCF<0.8)'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(res_group_list, range(len(res_group_list))):
data = master_low[master_low['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
except: pass
plt.show()
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - Low CCF (CCF<0.8)'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(group_list, range(len(group_list))):
data = master_low[master_low['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = palette_list[i], ax=ax)
except: pass
plt.show()
Co-Drivers¶
EGFR¶
In [116]:
master_EGFR = get_master_codrivers(master=master_no_wgd_cancer,
maf=maf_cohort_nowgd,
symbol='EGFR')
In [129]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - EGFR'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(res_group_list, range(len(res_group_list))):
data = master_EGFR[master_EGFR['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
except: pass
plt.show()
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - EGFR'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(group_list, range(len(group_list))):
data = master_EGFR[master_EGFR['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = palette_list[i], ax=ax)
except: pass
plt.show()
KRAS¶
In [130]:
master_KRAS = get_master_codrivers(master=master_no_wgd_cancer,
maf=maf_cohort_nowgd,
symbol='KRAS')
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - KRAS'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(res_group_list, range(len(res_group_list))):
data = master_KRAS[master_KRAS['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
except: pass
plt.show()
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - KRAS'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(group_list, range(len(group_list))):
data = master_KRAS[master_KRAS['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = palette_list[i], ax=ax)
except: pass
plt.show()
CDKN2A¶
In [131]:
master_CDKN2A = get_master_codrivers(master=master_no_wgd_cancer,
maf=maf_cohort_nowgd,
symbol='CDKN2A')
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - CDKN2A'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(res_group_list, range(len(res_group_list))):
data = master_CDKN2A[master_CDKN2A['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
except: pass
plt.show()
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - CDKN2A'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()
for group,i in zip(group_list, range(len(group_list))):
data = master_CDKN2A[master_CDKN2A['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
try:
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']), label= group)
kmf.plot_survival_function(color = palette_list[i], ax=ax)
except: pass
plt.show()
In [ ]: